In comparison to single-step retrosynthesis, multi-step retrosynthesis planning focuses on researching novel route search algorithms using a fixed single-step model to identify retrosynthetic disconnections. The pioneering work in the field uses neural-guided MCTS and a template-based approach to find synthesis routes [1]}. Instead of assessing the state value in the search tree at run-time, alternative methods use oracle functions to guide the tree search. These methods include DFPN search with edge cost, which combines classical DFPN with a neural heuristic [2]}, and Retro*, which combines the A* path finding algorithm with a neural heuristic [3]}. Newer approaches use a template-free model, either combining neural-guided MCTS with reaction feasibility heuristics [4]} or directly using synthesizability heuristics combined with a forward synthesis model [5]}. Instead of using heuristics, self-play [6]}, learning a value function by letting an algorithm play the game of synthesis against itself, is an additional investigated approach [7]}, [8]}, [9]}.
